Auto-Correlation of an Occupant Restraint System Model Using a Bayesian Validation Metric

Computer Aided Engineering (CAE) has become a vital tool for product development in automotive industry. Various computer models for occupant restraint systems are developed. The models simulate the vehicle interior, restraint system, and occupants in different crash scenarios. In order to improve the efficiency during the product development process, the model quality and its predictive capabilities must be ensured. In this research, an objective model validation metric is developed to evaluate the model validity and its predictive capabilities when multiple occupant injury responses are simultaneously compared with test curves. This validation metric is based on the probabilistic principal component analysis method and Bayesian statistics approach for multivariate model assessment. It first quantifies the uncertainties in both test and simulation results, extracts key features, and then evaluates the model quality. This paper presents a newly developed auto-correlation method using both the objective model validation metric and a genetic algorithm based advanced optimization approach to automatically select the best values of the model parameters. A successful application of the auto-correlation method is demonstrated by a case study.